Tone reproduction is an important issue in digital image enhancement. Tone reproduction relates to the representation of brightness and darkness in a digital image and the relationship of the representation to the original image. Tone reproduction is associated with several difficulties. Brightness and darkness are not always accurately represented in a captured digital image. Imaging circuitry utilized to capture the digital image may have several limitations. For example, the imaging circuitry may inject noise into the digital information or may present other non-linearities in the image capturing process.
Accordingly, techniques have been developed to improve the quality of tone reproduction. Two types of approaches are currently utilized: global mapping and local adaptive tone correction. Global mapping is a relatively straight-forward algorithm. However, its effectiveness is limited when applied to digital images that possess a high dynamic range. Local adaptive methods are generally utilized to enhance the tone reproduction of digital images that exhibit high dynamic ranges. Local adaptive algorithms seek to change a scaling factor based on local image features. For example, Chiu et al. proposed a non-uniform scaling function for rendering high dynamic range computer graphics in “Spatially non-uniform scaling functions for high contrasts images,” Proceedings of Graphics Interface '93, 1993. Moroney disclosed an image mask based tone correction algorithm for digital photographs in “Local color correction using non-linear masking,” IS&T SID 8th Color Imaging Conference, 2000. Although these algorithms are based on research of human eye behaviors, these algorithms produce noticeable gradient reversals known as a “halo effect.”
According to Moroney, low-pass filtering of an image is useful to avoid excessively reducing the image contrast. This is true in smooth image regions and in regions with slowly changing brightness. However, at high grayscale gradient regions, the blurred image mask produces a partially reversed band along the edges. This band becomes noticeable when one or both sides of the edge are large and smooth regions and, hence, gives rise to the halo effect.
To address the halo effect, a histogram adjustment algorithm based on human contrast sensitivity was proposed by Larson et al. in “A visibility matching tone reproduction operator for high dynamic range scenes,” IEEE Transactions on Visualization and Computer Graphics, vol. 3, no. 4, 1997. This algorithm overcomes the halo effect problem successfully, but it assumes that the actual brightness at each pixel location is known. This assumption is true for computer-created graphical images. However, this assumption is not true for digital images captured via digital photography. Accordingly, this algorithm is useful for only a limited subset of digital images.